Seunghan Kim^1,#, Changhoon Lim^2,#, Gwonsang Ryu³, Hyunil Kim^2,*

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.071190

Abstract Federated Learning enables privacy-preserving training of Transformer-based language models, but remains vulnerable to backdoor attacks that compromise model reliability. This paper presents a comparative analysis of defense strategies against both classical and advanced backdoor attacks, evaluated across autoencoding and autoregressive models. Unlike prior studies, this work provides the first systematic comparison of perturbation-based, screening-based, and hybrid defenses in Transformer-based FL environments. Our results show that screening-based defenses consistently outperform perturbation-based ones, effectively neutralizing most attacks across architectures. However, this robustness comes with significant computational overhead, revealing a clear trade-off between security and efficiency. By explicitly More >

Mona Almofarreh¹, Amnah Alshahrani², Nouf Helal Alharbi³, Ahmed Omer Ahmed⁴, Hussain Alshahrani⁵, Abdulrahman Alzahrani^6,*, Mohammed Mujib Alshahrani⁷, Asma A. Alhashmi⁸

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.070545

Abstract Zero-day attacks use unknown vulnerabilities that prevent being identified by cybersecurity detection tools. This study indicates that zero-day attacks have a significant impact on computer security. A conventional signature-based detection algorithm is not efficient at recognizing zero-day attacks, as the signatures of zero-day attacks are usually not previously accessible. A machine learning (ML)-based detection algorithm is proficient in capturing statistical features of attacks and, therefore, optimistic for zero-day attack detection. ML and deep learning (DL) are employed for designing intrusion detection systems. The improvement of absolute varieties of novel cyberattacks poses significant challenges for IDS… More >

Tamara Zhukabayeva^1,2, Zulfiqar Ahmad^1,3,*, Nurbolat Tasbolatuly⁴, Makpal Zhartybayeva¹, Yerik Mardenov^1,4, Nurdaulet Karabayev^1,*, Dilaram Baumuratova^1,4

CMES-Computer Modeling in Engineering & Sciences, DOI:10.32604/cmes.2025.070426

Abstract The Industrial Internet of Things (IIoT), combined with the Cyber-Physical Systems (CPS), is transforming industrial automation but also poses great cybersecurity threats because of the complexity and connectivity of the systems. There is a lack of explainability, challenges with imbalanced attack classes, and limited consideration of practical edge–cloud deployment strategies in prior works. In the proposed study, we suggest an Impact-Aware Taxonomy-Driven Machine Learning Framework with Edge Deployment and SHapley Additive exPlanations (SHAP)-based Explainable AI (XAI) to attack detection and classification in IIoT-CPS settings. It includes not only unsupervised clustering (K-Means and DBSCAN) to extract… More >

Hong Zhao, Hongbin Chen^*, Zhihui Guo, Ling Zhan, Shichao Li

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072776

Abstract UAV-mounted intelligent reflecting surface (IRS) helps address the line-of-sight (LoS) blockage between sensor nodes (SNs) and the fusion center (FC) in Internet of Things (IoT). This paper considers an IoT assisted by multiple UAVs-mounted IRS (U-IRS), where the data from ground SNs are transmitted to the FC. In practice, energy efficiency (EE) and mission completion time are crucial metrics for evaluating system performance and operational costs. Recognizing their importance during data collection, we formulate a multi-objective optimization problem to maximize EE and minimize total mission completion time simultaneously. To characterize this tradeoff while considering optimization… More >

Sabina-Cristiana Necula^*, Napoleon-Alexandru Sireteanu

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070861

Abstract Modern business information systems face significant challenges in managing heterogeneous data sources, integrating disparate systems, and providing real-time decision support in complex enterprise environments. Contemporary enterprises typically operate 200+ interconnected systems, with research indicating that 52% of organizations manage three or more enterprise content management systems, creating information silos that reduce operational efficiency by up to 35%. While attention mechanisms have demonstrated remarkable success in natural language processing and computer vision, their systematic application to business information systems remains largely unexplored. This paper presents the theoretical foundation for a Hierarchical Attention-Based Business Information System (HABIS)… More >

Haibo Lei^1,2, Xu An Wang^1,*, Wenhao Liu¹, Lingling Wu¹, Chao Zhang¹, Weiwei Jiang³, Xiao Zou⁴

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.070030

Abstract With the rapid expansion of the Internet of Things (IoT), user data has experienced exponential growth, leading to increasing concerns about the security and integrity of data stored in the cloud. Traditional schemes relying on untrusted third-party auditors suffer from both security and efficiency issues, while existing decentralized blockchain-based auditing solutions still face shortcomings in correctness and security. This paper proposes an improved blockchain-based cloud auditing scheme, with the following core contributions: Identifying critical logical contradictions in the original scheme, thereby establishing the foundation for the correctness of cloud auditing; Designing an enhanced mechanism that… More >

Dong Hyun Lee¹, Na Kyung Lee², Young Seo Lee^1,2,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.069976

Abstract There has been an increasing emphasis on performing deep neural network (DNN) inference locally on edge devices due to challenges such as network congestion and security concerns. However, as DRAM process technology continues to scale down, the bit-flip errors in the memory of edge devices become more frequent, thereby leading to substantial DNN inference accuracy loss. Though several techniques have been proposed to alleviate the accuracy loss in edge environments, they require complex computations and additional parity bits for error correction, thus resulting in significant performance and storage overheads. In this paper, we propose FeatherGuard,… More >

SEUNGIL KIM, GUN-DONG KIM^*

BIOCELL, DOI:10.32604/biocell.2025.073781

Abstract Neutrophil extracellular trap (NET) formation or NETosis is a specialized innate immune process in which neutrophils release chromatin fibers decorated with histones and antimicrobial proteins. Although pivotal for pathogen clearance, aberrant NETosis has emerged as a critical modulator of acute and chronic respiratory pathologies, including acute respiratory distress syndrome, asthma, and chronic obstructive pulmonary disease. Dysregulated NET release exacerbates airway inflammation by inducing epithelial injury, mucus hypersecretion, and the recruitment of inflammatory leukocytes, thereby accelerating tissue remodeling and functional decline. Mechanistically, NETosis is governed by peptidyl arginine deiminase 4 (PADI4)-mediated histone citrullination, NADPH oxidase-dependent reactive More >

Qi Feng^1,2, Weijie Hu¹, Lu Liu^3,*, Junhui Luo⁴

Structural Durability & Health Monitoring, DOI:10.32604/sdhm.2025.072955

Abstract Ultra-high performance fiber-reinforced concrete (UHPFRC) has received extensive attention from scholars and engineers due to its excellent mechanical properties and durability. However, there is a mutually restrictive relationship between the workability and mechanical properties of UHPFRC. Specifically, the addition of fibers will affect the workability of fresh UHPFRC, and the workability of fresh UHPFRC will also affect the dispersion and arrangement of fibers, thus significantly influencing the mechanical properties of hardened UHPFRC. This paper first analyzes the research status of UHPFRC and the relationship between its workability and mechanical properties. Subsequently, it outlines the test… More >

Nada Zaari Jabri¹, Mohamed Ait-El-Mokhtar^1,*, Fadoua Mekkaoui¹, Najwa Rabah¹, Ilham Amghar¹, Ghizlane Diria², Abdelaziz Hmyene¹

Phyton-International Journal of Experimental Botany, DOI:10.32604/phyton.2025.071634

Abstract Cadmium (Cd) contamination is a major environmental stressor that adversely affects crop germination and early development. This study assessed the impact of increasing Cd concentrations (0.125 to 1 g/L) on seed germination and early seedling growth in three bread wheat (Triticum aestivum L.) cultivars: Achtar, Lina, and Snina. The results revealed a clear dose-dependent inhibitory effect of Cd. Germination percentage (GP) significantly declined with increasing Cd levels, while mean germination time was progressively delayed, particularly at higher concentrations. Vigor index (VI) also showed significant reductions, reflecting compromised seedling establishment. Morphological traits, especially shoot and root lengths,… More >